Harnessing Deep Learning for Fault Detection in Industry 4.0: A Multimodal Approach

The advent of Industry 4.0 has reshaped the modern industries (e.g., Manufacturing, Automotive, Aerospace and Defense), driven by the rapid development of artificial intelligence, smart sensing technologies, and interconnected cyber-physical systems. One of the most important goal for Industry 4.0 is the improvement and enhancement in productivity, reduction of production losses, and operational efficiency. High quality fault detection within different environments is the foundation to achieve these goals. Industry 4.0 emphasizes the use of interconnected systems, smart sensors, and advanced analytics, which generate large volumes of data from production environments. The data complexity and scale, product by the widespread deployment of sensors and IoT devices, demands sophisticated Machine Learning (ML) and data-driven methods. In this paper, we argue that the future of accurate and reliable fault detection lies in the seamless fusion of edge-cloud computing, explainable AI, and adaptive machine learning algorithms capable of processing high-frequency, multimodal data streams in real time. Different kinds of advantages for multimondal learning and core enabling techniques are also discussed and revise. This vision paper outlines the future trajectory of multimodal learning based fault detection, via identifying critical research gaps and promising directions for future study.